AzureML terminology change - Azure Direct Models

Author: msakande

articles/machine-learning/concept-data-privacy.md

@@ -29,7 +29,7 @@ When you deploy models in Azure Machine Learning, the following types of data ar
 
 Deploying models to managed compute deploys model weights to dedicated Virtual Machines and exposes a REST API for real-time inference. Learn more about deploying models from the [Model Catalog to managed compute](concept-model-catalog.md). You manage the infrastructure for these managed computes, and Azure's data, privacy, and security commitments apply. Learn more about [Azure compliance offerings](https://servicetrust.microsoft.com/DocumentPage/7adf2d9e-d7b5-4e71-bad8-713e6a183cf3) applicable to Azure Machine Learning.
 
-Although containers for models "Curated by Azure AI" are scanned for vulnerabilities that could exfiltrate data, not all models available through the model catalog have been scanned. To reduce the risk of data exfiltration, you can protect your deployment using virtual networks. Follow this link to [learn more](./how-to-network-isolation-model-catalog.md). You can also use [Azure Policy](./how-to-regulate-registry-deployments.md) to regulate the models that can be deployed by your users.
+Although containers for "Azure Direct Models" are scanned for vulnerabilities that could exfiltrate data, not all models available through the model catalog have been scanned. To reduce the risk of data exfiltration, you can protect your deployment using virtual networks. Follow this link to [learn more](./how-to-network-isolation-model-catalog.md). You can also use [Azure Policy](./how-to-regulate-registry-deployments.md) to regulate the models that can be deployed by your users.
 
 :::image type="content" source="media/concept-data-privacy/platform-service.png" alt-text="A diagram showing the platform service life cycle." lightbox="media/concept-data-privacy/platform-service.png":::
 

articles/machine-learning/concept-models-featured.md

@@ -368,4 +368,4 @@ For examples of how to use Stability AI models, see the following examples:
 - [Deploy models as standard deployment](how-to-deploy-models-serverless.md)
 - [Model catalog and collections](concept-model-catalog.md)
 - [Region availability for models in standard deployment](concept-endpoint-serverless-availability.md)
-- [Content safety for models curated by Azure AI in the model catalog](../ai-foundry/concepts/model-catalog-content-safety.md)
+- [Content safety for Azure Direct Models](../ai-foundry/concepts/model-catalog-content-safety.md)

articles/machine-learning/how-to-network-isolation-model-catalog.md

@@ -45,17 +45,17 @@ Since the workspace managed virtual network can access internet in this configur
 1. Configure a workspace by following [Workspace managed network isolation](./how-to-managed-network.md#configure-a-managed-virtual-network-to-allow-only-approved-outbound). In step 3 of the tutorial when selecting **Workspace managed outbound access**, select **Allow Only Approved Outbound**.  
 2. If you set the public network access to the workspace to disabled, you can connect to the workspace using one of the methods as listed [in step 2 of the allow internet outbound section of this tutorial](#workspace-managed-virtual-network-to-allow-internet-outbound).
 3. The workspace manages virtual network is set to an allow only configuration. You must add a corresponding user-defined outbound rule to allow all the relevant FQDNs.
-    1. Follow this link for a list of FQDNs required for the [Curated by Azure AI collection](#language-models-in-curated-by-azure-ai-collection).
+    1. Follow this link for a list of FQDNs required for the [Azure Direct Models collection](#language-models-in-curated-by-azure-ai-collection).
     2. Follow this link for a list of FQDNs required for the [Hugging Face collection](#work-with-hugging-face-collection). 
 
 ## Work with open source models curated by Azure Machine Learning
 
 Workspace managed virtual network to allow only approved outbound uses a Service Endpoint Policy to Azure Machine Learning managed storage accounts, to help access the models in the collections curated by Azure Machine Learning in an out-of-the-box manner. This mode of workspace configuration also has default outbound to the Microsoft Container Registry that contains the docker image used to deploy the models. 
 
-### Language models in 'Curated by Azure AI' collection
+### Language models in 'Azure Direct Models' collection
 
 These models involve dynamic installation of dependencies at runtime. To add a user defined outbound rule, follow step four of 
-To use the Curated by Azure AI collection, users should add user defined outbound rules for the following FQDNs at the workspace level:
+To use the Azure Direct Models collection, users should add user defined outbound rules for the following FQDNs at the workspace level:
 
   * `*.anaconda.org`
   * `*.anaconda.com`